Search results

The paper aims to analyse the meaning and extension of discretionary power of social service professionals within network-based interventions.

Empirically, the paper is based on a case study of a network-based policy involving private and public organisations in the Northeast of Italy (Province of Trento).

The paper identifies netocracy as a social policy logic distinct from bureaucracy and professionalism. What legitimises netocracy is neither authority nor expertise but cooperation, the activation of connections and involvement, considered “good” per se. In this framework, professionalism and discretion acquire new and problematic meanings compared to street-level bureaucracy processes.

Based on a case study, the research results cannot be generalised but pave the way to further comparative investigations.

The paper reveals that the position of professionals in netocracy is to some extent trickier than that in a bureaucracy because netocracy seems to have the power to encapsulate them and make it less likely for them to deviate from expected courses of action.

Combining different literature streams – street level bureaucracy, professionalism, network organisations and welfare governance – and building on an original case study, the paper contribute to understanding professionalism in welfare contexts increasingly characterised by the combination of bureaucratic, professional and network logics.

High-tech enterprises, as the national innovation powerhouses, have garnered considerable interest, particularly regarding their technological innovation capabilities. Nevertheless, prevalent research tends to spotlight the impact of individual factors on innovative behavior, with only a fraction adopting a comprehensive viewpoint, scrutinizing the causal amalgamations of precursor conditions influencing the overall innovation proficiency of high-tech enterprises.

This paper employs a hybrid approach integrating necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA) to examine the combinatorial effects of antecedent factors on high-tech enterprises' innovation output. Our analysis draws upon data from 46 listed Chinese high-tech enterprises. To promote technological innovation within high-tech enterprises, we introduce a novel perspective that emphasizes technological innovation networks, grounded in a network agents-structure-environment framework. These antecedents are government subsidy, tax benefits, customer concentration, purchase concentration rate, market-oriented index and innovation environment.

The findings delineate four configurational pathways leading to high innovative output and three pathways resulting in low production.

This study thereby enriches the body of knowledge around technological innovation and provides actionable policy recommendations.

The role of chief executive officer (CEO) personal characteristics in shaping corporate policies has attracted increasing academic attention in the past two decades. In this review, the authors synthesize extant research on CEO attributes by reviewing 232 articles published in 29 journals from the accounting, finance and management literature. This review provides an overview of existing findings, highlights current trends and interdisciplinary differences in research approaches and identifies potential avenues for future research.

To review the literature on CEO attributes, the authors manually collected peer-reviewed articles in accounting, finance and management journals from 2000 to 2021. The authors conducted in-depth analysis of each paper and manually recorded the theories, data sources, country of study, study period, measures of CEO attributes and dependent variables. This procedure helped the authors group the selected articles into themes and sub-themes. The authors compared the findings in various disciplines and provided direction for future research.

The authors highlight the role of CEO personal attributes in influencing corporate decision-making and firm outcomes. The authors categorize studies of CEO traits into three main research themes: (1) demographic attributes and experience (including age, gender, culture, experience, education); (2) CEO interactions with others (social and political networks) and (3) underlying attributes (including personality, values and ideology). The evidence shows that CEO characteristics significantly affect a wide range of specific corporate policies that serve as mechanisms through which individual CEOs determine firm success and performance.

CEO selection is one of the most crucial decisions made by corporations. The study findings provide valuable insights to corporate executives, boards, investors and practitioners into how CEOs’ personal characteristics can impact future firm decisions and outcomes that can, in turn, inform the high-stake process of CEO recruitment and selection. The study findings have significant practical implications for corporations, such as contributing to executive training programs, to assist executives and directors attain a greater level of self-awareness.

Building on the theoretical foundation of upper echelons theory, the authors offer an integrated theoretical framework to consolidate existing empirical research on the impacts of CEO personal attributes on firm outcomes across accounting and finance (A&F) and management literature. The study findings provide a roadmap for scholars to bridge the interdisciplinary divide between A&F and management research. The authors advocate a more holistic and multifaceted approach to examining CEOs, each of whom embodies a myriad of personal characteristics that comprise their unique identity. The study findings encourage future researchers to expand the investigation of the boundary conditions that magnify or moderate the impacts of CEO idiosyncrasies.

This paper aims to study the agent learning from expert demonstration data while incorporating reinforcement learning (RL), which enables the agent to break through the limitations of expert demonstration data and reduces the dimensionality of the agent’s exploration space to speed up the training convergence rate.

Firstly, the decay weight function is set in the objective function of the agent’s training to combine both types of methods, and both RL and imitation learning (IL) are considered to guide the agent's behavior when updating the policy. Second, this study designs a coupling utilization method between the demonstration trajectory and the training experience, so that samples from both aspects can be combined during the agent’s learning process, and the utilization rate of the data and the agent’s learning speed can be improved.

The method is superior to other algorithms in terms of convergence speed and decision stability, avoiding training from scratch for reward values, and breaking through the restrictions brought by demonstration data.

The agent can adapt to dynamic scenes through exploration and trial-and-error mechanisms based on the experience of demonstrating trajectories. The demonstration data set used in IL and the experience samples obtained in the process of RL are coupled and used to improve the data utilization efficiency and the generalization ability of the agent.

This study aims to elucidate institutional analysis as an effective approach to investigating and designing the multilevel policymaking system of online social networks (OSN) for achieving a participatory model.

The institutional mapping approach has been used to analyze Iran’s OSN multilevel policymaking system. A combination of two matrices, including institutions-institutions and institutions-functions, was used to perform the institutional mapping. Two main steps were taken to draw the mentioned matrices. First, a review of related studies in Iran’s OSN policymaking system was conducted and the policy functions mentioned in these studies were identified and categorized using the meta-synthesis. Second, based on analyzing two policy documents of Iran’s OSN, institutions and their interactions were identified and policy functions were allocated to institutions.

Based on the results, the most important policy functions in the current OSN policymaking system in Iran are support, regulatory, monitoring and evaluation, business environment development, culture building and promotion, organizing licenses and permissions, policymaking and legislation. Also, the results show that there are shortcomings in this system, some of the most important of which are lack of transparency in regulatory, little work in culture building and promotion, neglect of the training of specialized human resources and research and development, slow development of the business environment and neglecting the role of nongovernmental organizations in policymaking.

By examining and analyzing how different institutions operate within a multilevel policymaking system, the policymaking process and its overall effectiveness can be enhanced. This analysis helps identify any inconsistencies, overlaps or conflicts in the roles and policies of these institutions, leading to a better understanding of how a multilevel policymaking system is organized.

This chapter analyzes the link between the digital divide, infrastructure regulation, and disaster planning and relief through a case study of the flood in San Jose, California triggered by the Anderson dam’s overtopping in February 2017 and an examination of communication failures during the 2018 wildfire in Paradise, California. This chapter theorizes that regulatory decisions construct social and disaster vulnerability. Rooted in the Whole Community approach to disaster planning and relief espoused by the United Nations and the Federal Emergency Management Agency, this chapter calls for leadership to end the digital divide. It highlights the imperative of understanding community information needs and argues for linking strategies to close the digital divide with infrastructure and emergency planning. As the Internet’s integration into society increases, the digital divide diminishes access to societal resources including disaster aid, and exacerbates wildfire, flood, pandemic, and other risks. To mitigate climate change, climate-induced disaster, protect access to social services and the economy, and safeguard democracy, it argues for digital inclusion strategies as a centerpiece of community-centered infrastructure regulation and disaster relief.

Because of the various advantages of reinforcement learning (RL) mentioned above, this study uses RL to train unmanned aerial vehicles to perform two tasks: target search and cooperative obstacle avoidance.

This study draws inspiration from the recurrent state-space model and recurrent models (RPM) to propose a simpler yet highly effective model called the unmanned aerial vehicles prediction model (UAVPM). The main objective is to assist in training the UAV representation model with a recurrent neural network, using the soft actor-critic algorithm.

This study proposes a generalized actor-critic framework consisting of three modules: representation, policy and value. This architecture serves as the foundation for training UAVPM. This study proposes the UAVPM, which is designed to aid in training the recurrent representation using the transition model, reward recovery model and observation recovery model. Unlike traditional approaches reliant solely on reward signals, RPM incorporates temporal information. In addition, it allows the inclusion of extra knowledge or information from virtual training environments. This study designs UAV target search and UAV cooperative obstacle avoidance tasks. The algorithm outperforms baselines in these two environments.

It is important to note that UAVPM does not play a role in the inference phase. This means that the representation model and policy remain independent of UAVPM. Consequently, this study can introduce additional “cheating” information from virtual training environments to guide the UAV representation without concerns about its real-world existence. By leveraging historical information more effectively, this study enhances UAVs’ decision-making abilities, thus improving the performance of both tasks at hand.

The purpose of this study is to investigate whether and how inter-firm cooperation and firm age moderate the relationship between family ownership and productivity.

We first estimate the total factor productivity (TFP) of a large sample of Italian firms observed over the period 2010–2018 and then apply a Poisson random effects model.

TFP is, on average, higher for non-family firms (non-FFs) than for FF. Furthermore, inter-organizational cooperation and firm age mitigate the negative effect of family ownership. In detail, it is found that belonging to a network acts as a moderator in different ways according to firm age. Indeed, young FFs underperform non-FF peers, although the TFP gap decreases with age. In contrast, the benefits of a formal network are high for older FFs, suggesting that an age-related learning process is at work.

The study provides evidence that FFs can outperform non-FFs when they move away from Socio-Emotional Wealth-centered reference points and exploit knowledge flows arising from high levels of social capital. In the case of mature FFs, networking is a driver of TFP, allowing them to acquire external resources. Since FFs often do not have sufficient in-house knowledge and resources, they must be aware of the value of business cooperation. While preserving the familiar identity of small companies, networks grant FFs the competitive and scale advantages of being large.

Despite the wide but ambiguous body of research on the performance gap between FFs and non-FFs, little is known about the role of FFs’ heterogeneity. This study has proven successful in detecting age as a factor in heterogeneity, specifically to explain the network effect on the link between ownership and TFP. Based on a representative sample, the study provides a solid framework for FFs, policymakers and academic research on family-owned companies.

This pan-Canadian study examines the information practices of STEM-trained immigrant women to Canada as they navigate workfinding and workplace integration. Our study focuses on a population of highly skilled immigrant women from across Canada and uses an information practice lens to examine their lived experiences of migration and labour market integration. As highly trained STEM professionals in pursuit of employment, our participants have specific needs and challenges, and as we explore these, we consider the intersection of their information practices with government policies, settlement services and the hiring practices of STEM employers.

We conducted a qualitative study using in-depth interviews with 74 immigrant women across 13 Canadian provinces and territories to understand the nature of their engagement with employment-seeking in STEM sectors. This article reports the findings related to the settlement and information experiences of the immigrant women as they navigate new information landscapes.

As immigrants, as women and as STEM professionals, the experiences of the 74 participants reflect both marginality and privilege. The reality of their intersectional identities is that these women may not be well-served by broader settlement resources targeting newcomers, but neither are the specific conventions of networking and job-seeking in the STEM sectors in Canada fully apparent or accessible to them. The findings also point to the broader systemic and contextual factors that participants have to navigate and that shape in a major way their workfinding journeys.

The findings of this pan-Canadian study have theoretical and practical implications for policy and research. Through interviews with these STEM professionals, we highlight the barriers and challenges of an under-studied category of migrants (the highly skilled and “desirable” type of immigrants). We provide a critical discussion of their settlement experiences and expose the idiosyncrasies of a system that claims to value skilled talent while structurally making it very difficult to deliver on its promises to recruit and retain highly qualified personnel. Our findings point to specific aspects of these skilled professionals’ experiences, as well as the broader systemic and contextual factors that shape their workfinding journey.

Understandings of house prices and their interrelationships have undoubtedly drawn a great amount of attention from various market participants. This study aims to investigate the monthly newly-built residential house price indices of seventy Chinese cities during a 10-year period spanning January 2011–December 2020 for understandings of issues related to their interdependence and synchronizations.

Analysis here is facilitated through network analysis together with topological and hierarchical characterizations of price comovements.

This study determines eight sectoral groups of cities whose house price indices are directly connected and the price synchronization within each group is higher than that at the national level, although each shows rather idiosyncratic patterns. Degrees of house price comovements are generally lower starting from 2018 at the national level and for the eight sectoral groups. Similarly, this study finds that the synchronization intensity associated with the house price index of each city generally switches to a lower level starting from early 2019.

Results here should be of use to policy design and analysis aiming at housing market evaluations and monitoring.